System for marketing touchpoint attribution bias correction

Abstract

The present disclosure provides a detailed description of techniques used in systems, methods, and computer program products for marketing touchpoint attribution bias correction. More specifically, the herein disclosed techniques enable identifying a collection of marketing touchpoints (e.g., associated with a desired conversion) and receiving touchpoint data and/or conversion data associated with those touchpoints. The received data is used to determine contribution values for each of the respective touchpoints that indicate the probability of conversion generated by the respective touchpoints. Some contribution values can have attribution biases. Such biases are addressed by identifying a low contribution value associated with the collection of touchpoints and reducing or eliminating the low contribution value from the remaining contribution values to generate corrected contribution values for the remaining touchpoints.

What is claimed is: 
     
         1 . A computer implemented method for allocating credit for conversions among advertising touchpoints, the computer implemented method comprising:
 storing in a computer, a plurality of touchpoint encounters that represent marketing messages exposed to a plurality of users;   sorting data for the touchpoint encounters in the computer to separate into converting user data, which comprises touchpoint encounters for users that exhibited a positive response to the marketing message, and non-converting user data that comprises touchpoint encounters for users that exhibited a negative response to the marketing message;   retrieving, from storage, the converting user data and the non-converting user data;   training, using machine-learning techniques in a computer, the converting user data and the non-converting user data as training data to determine a set of contribution values for each of the touchpoints, wherein the contribution values for a touchpoint reflect importance of the respective touchpoint, relative to other touchpoints, to the response of the marketing message;   identifying a low contribution value by comparing the contribution values of the respective touchpoints; and   generating at least one corrected contribution value based at least in part on the low contribution value and at least one of the contribution values.   
     
     
         2 . The method of  claim 1 , wherein the contribution values characterize a measure of an influence attributed to the respective touchpoints in transitioning at least one user from a first engagement state to a second engagement state. 
     
     
         3 . The method of  claim 1 , wherein generating the corrected contribution value comprises subtracting the low contribution value from at least one of the contribution values. 
     
     
         4 . The method of  claim 1 , further comprising generating one or more touchpoint combination contribution values by combining two or more contribution values. 
     
     
         5 . The method of  claim 4 , further comprising generating at least one corrected touchpoint combination contribution value based at least in part on the low contribution value and at least one of the touchpoint combination contribution values. 
     
     
         6 . The method of  claim 5 , wherein generating the corrected touchpoint combination contribution value comprises subtracting the low contribution value from at least one of the touchpoint combination contribution values. 
     
     
         7 . The method of  claim 1 , further comprising generating at least one contribution value range based at least in part on the corrected contribution value and at least one of the contribution values. 
     
     
         8 . The method of  claim 1 , further comprising generating at least one additional corrected contribution value based at least in part on the low contribution value and one or more contribution values associated with one or more respective additional touchpoints. 
     
     
         9 . The method of  claim 1 , wherein the respective touchpoints are associated with an engagement stack. 
     
     
         10 . The method of  claim 1 , wherein the respective touchpoints are associated with a common touchpoint attribute. 
     
     
         11 . The method of  claim 1 , wherein the respective touchpoints are associated with a common desired response. 
     
     
         12 . The method of  claim 1 , wherein the respective touchpoints are associated with a set of common audience demographics. 
     
     
         13 . A computer program, embodied in a non-transitory computer readable medium, the non-transitory computer readable medium having stored thereon a sequence of instructions which, when stored in memory and executed by a processor causes the processor to perform a set of acts, the acts comprising:
 storing in a computer, a plurality of touchpoint encounters that represent marketing messages exposed to a plurality of users;   sorting data for the touchpoint encounters in the computer to separate into converting user data, which comprises touchpoint encounters for users that exhibited a positive response to the marketing message, and non-converting user data that comprises touchpoint encounters for users that exhibited a negative response to the marketing message;   retrieving, from storage, the converting user data and the non-converting user data;   training, using machine-learning techniques in a computer, the converting user data and the non-converting user data as training data to determine a set of contribution values for each of the touchpoints, wherein the contribution values for a touchpoint reflect importance of the respective touchpoint, relative to other touchpoints, to the response of the marketing message;   identifying a low contribution value by comparing the contribution values of the respective touchpoints; and   generating at least one corrected contribution value based at least in part on the low contribution value and at least one of the contribution values.   
     
     
         14 . The computer readable medium of  claim 13 , wherein the contribution values characterize a measure of an influence attributed to the respective touchpoints in transitioning at least one user from a first engagement state to a second engagement state. 
     
     
         15 . The computer readable medium of  claim 13 , wherein generating the corrected contribution value comprises subtracting the low contribution value from at least one of the contribution values. 
     
     
         16 . The computer readable medium of  claim 13 , further comprising instructions which, when stored in memory and executed by a processor causes the processor to perform generating one or more touchpoint combination contribution values by combining two or more contribution values. 
     
     
         17 . The computer readable medium of  claim 16 , further comprising instructions which, when stored in memory and executed by a processor causes the processor to perform generating at least one corrected touchpoint combination contribution value based at least in part on the low contribution value and at least one of the touchpoint combination contribution values. 
     
     
         18 . The computer readable medium of  claim 17 , wherein generating the corrected touchpoint combination contribution value comprises subtracting the low contribution value from at least one of the touchpoint combination contribution values. 
     
     
         19 . As system comprising:
 a storage device to store in a computer, a plurality of touchpoint encounters that represent marketing messages exposed to a plurality of users; and   a processor for executing instructions which, when stored in a memory and executed by the processor causes the processor to perform,
 sorting data for the touchpoint encounters to separate into converting user data, which comprises touchpoint encounters for users that exhibited a positive response to the marketing message, and non-converting user data that comprises touchpoint encounters for users that exhibited a negative response to the marketing message; 
 retrieving, from storage, the converting user data and the non-converting user data; 
 training, using machine-learning techniques in a computer, the converting user data and the non-converting user data as training data to determine a set of contribution values for each of the touchpoints, wherein the contribution values for a touchpoint reflect importance of the respective touchpoint, relative to other touchpoints, to the response of the marketing message; 
 identifying a low contribution value by comparing the contribution values of the respective touchpoints; and 
 generating at least one corrected contribution value based at least in part on the low contribution value and at least one of the contribution values. 
   
     
     
         20 . The system of  claim 19 , wherein the contribution values characterize a measure of an influence attributed to the respective touchpoints in transitioning at least one user from a first engagement state to a second engagement state.